The table below provides demographic information based on severity of injury
# level naming for categorical variables
df_demo$gender <- factor(df_demo$gender,
levels = c(1,2,3),
labels = c("Male", "Female", "Nonbinary"))
df_demo$work_current <- factor(df_demo$work_current,
levels = c(1,0),
labels = c("Yes", "No"))
df_demo$severity <- factor(df_demo$severity,
levels = c(2,3),
labels = c("Moderate", "Severe"))
df_demo$mech_injury <- factor(df_demo$mech_injury,
levels = c(1,2,3,4,5),
labels = c("Fall", "MVC", "Sports", "Violence", "Pedestrian struck"))
df_demo$income <- factor(df_demo$income,
levels = c(1,2,3),
labels = c("<52K", "52K-156K", ">156K"))
df_demo$marital_status <- factor(df_demo$marital_status,
levels = c(1, 2, 3, 4),
labels = c("Single", "Married", "Divorced", "Widowed"))
| Characteristic | N | Moderate, N = 191 | Severe, N = 271 | p-value2 |
|---|---|---|---|---|
| Age (years) | 46 | 51 (14) | 44 (14) | 0.091 |
| Time since TBI (years) | 46 | 7 (5) | 10 (8) | 0.14 |
| Gender | 46 |
|
|
0.20 |
| Male |
|
7 (37%) | 16 (59%) |
|
| Female |
|
11 (58%) | 11 (41%) |
|
| Nonbinary |
|
1 (5.3%) | 0 (0%) |
|
| Education (years) | 46 | 15.47 (2.04) | 15.11 (2.61) | 0.60 |
| Race/Ethnicity | 46 |
|
|
0.24 |
| Asian |
|
0 (0%) | 2 (7.4%) |
|
| Biracial |
|
2 (11%) | 0 (0%) |
|
| Black |
|
0 (0%) | 1 (3.7%) |
|
| Hispanic |
|
1 (5.3%) | 3 (11%) |
|
| White |
|
16 (84%) | 21 (78%) |
|
| Employment status | 46 |
|
|
0.69 |
| Yes |
|
9 (47%) | 10 (37%) |
|
| No |
|
10 (53%) | 17 (63%) |
|
| Annual household income | 46 |
|
|
0.84 |
| <52K |
|
6 (32%) | 10 (37%) |
|
| 52K-156K |
|
9 (47%) | 13 (48%) |
|
| >156K |
|
4 (21%) | 4 (15%) |
|
| Size household | 46 | 2.00 (1.05) | 2.19 (1.36) | 0.61 |
| Marital status | 46 |
|
|
0.11 |
| Single |
|
5 (26%) | 15 (56%) |
|
| Married |
|
11 (58%) | 8 (30%) |
|
| Divorced |
|
3 (16%) | 4 (15%) |
|
| Widowed |
|
0 (0%) | 0 (0%) |
|
| Substance use score | 46 | 4.16 (3.62) | 1.67 (1.92) | 0.011 |
| Cause of injury | 46 |
|
|
0.12 |
| Fall |
|
10 (53%) | 5 (19%) |
|
| MVC |
|
4 (21%) | 11 (41%) |
|
| Sports |
|
1 (5.3%) | 4 (15%) |
|
| Violence |
|
1 (5.3%) | 4 (15%) |
|
| Pedestrian struck |
|
3 (16%) | 3 (11%) |
|
| 1 Mean (SD); n (%) | ||||
| 2 Welch Two Sample t-test; Pearson’s Chi-squared test | ||||
ACS3 (activity re-engagement scores - outcome measure) by severity of injury
| Characteristic | N | Moderate, N = 191 | Severe, N = 271 | p-value2 |
|---|---|---|---|---|
| ACS Global Before | 46 | 72 (11) | 68 (10) | 0.18 |
| ACS Global Current | 46 | 54 (17) | 52 (12) | 0.63 |
| Global Retained (%) | 46 | 75 (19) | 77 (15) | 0.72 |
| ACS IADL Before | 46 | 22.16 (2.27) | 20.96 (3.29) | 0.15 |
| ACS IADL Current | 46 | 17.9 (4.9) | 16.8 (4.4) | 0.43 |
| IADL Retained (%) | 46 | 81 (19) | 81 (18) | 0.98 |
| ACS Leisure Before | 46 | 22.5 (5.8) | 20.9 (4.6) | 0.33 |
| ACS Leisure Current | 46 | 18.0 (6.0) | 16.6 (5.0) | 0.41 |
| Leisure Retained (%) | 46 | 82 (22) | 80 (16) | 0.75 |
| ACS Fitness Before | 46 | 13.2 (4.4) | 12.8 (4.4) | 0.76 |
| ACS Fitness Current | 46 | 8.3 (4.7) | 8.4 (3.3) | 0.93 |
| Fitness Retained (%) | 46 | 64 (32) | 69 (34) | 0.55 |
| ACS Social Before | 46 | 13.95 (1.22) | 12.89 (1.55) | 0.013 |
| ACS Social Current | 46 | 9.68 (3.08) | 9.91 (2.48) | 0.79 |
| Social Retained (%) | 46 | 69 (19) | 77 (18) | 0.16 |
| 1 Mean (SD) | ||||
| 2 Welch Two Sample t-test | ||||
Below is the ttest for the specific t and p value for the difference between ACS3 previous social score, which was significantly different.
##
## Welch Two Sample t-test
##
## data: df_mod$acss_prev and df_severe$acss_prev
## t = 2.5819, df = 43.36, p-value = 0.01328
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 0.231927 1.885032
## sample estimates:
## mean of x mean of y
## 13.94737 12.88889
Comparison of self-regulation scores by severity
| Characteristic | N | Moderate, N = 191 | Severe, N = 271 | p-value2 |
|---|---|---|---|---|
| Executive function | 46 | 41 (10) | 42 (11) | 0.82 |
| Disinhibition | 46 | 32.3 (6.1) | 33.0 (5.9) | 0.67 |
| Apathy | 46 | 33 (8) | 34 (9) | 0.61 |
| Total FrSBe Score | 46 | 106 (19) | 109 (21) | 0.65 |
| 1 Mean (SD) | ||||
| 2 Welch Two Sample t-test | ||||
Comparison of subscales of TBI QOL measure by severity of injury
| Characteristic | N | Moderate, N = 191 | Severe, N = 271 | p-value2 |
|---|---|---|---|---|
| Participation SRA | 46 | 47 (7) | 46 (6) | 0.48 |
| Anger | 46 | 53 (9) | 50 (10) | 0.29 |
| Anxiety | 46 | 58 (7) | 54 (10) | 0.13 |
| Communication | 46 | 46 (9) | 46 (10) | 0.95 |
| Depression | 46 | 55 (8) | 53 (11) | 0.57 |
| Dyscontrol | 46 | 52 (8) | 50 (9) | 0.44 |
| EF | 46 | 34.9 (4.8) | 35.9 (6.9) | 0.56 |
| Fatigue | 46 | 57 (8) | 54 (9) | 0.29 |
| Gen Cognition | 46 | 36 (8) | 37 (10) | 0.74 |
| Headache | 46 | 51 (9) | 48 (9) | 0.41 |
| Mobility | 46 | 47 (10) | 44 (8) | 0.28 |
| Pain | 46 | 58 (11) | 54 (10) | 0.21 |
| Positive Effect | 46 | 50 (6) | 50 (8) | 0.99 |
| Resilience | 46 | 49 (6) | 48 (9) | 0.56 |
| Satisfaction SRA | 46 | 46 (6) | 45 (7) | 0.49 |
| Self esteem | 46 | 47 (11) | 49 (11) | 0.69 |
| Stigma | 45 | 50 (8) | 51 (7) | 0.62 |
| Upper Extremity | 46 | 47 (9) | 42 (8) | 0.078 |
| 1 Mean (SD) | ||||
| 2 Welch Two Sample t-test | ||||
Comparison of composite scores for TBI QOL by severity of injury. Composite scores were calculated using:
Tyner, C. E., Boulton, A. J., Sherer, M., Kisala, P. A., Glutting, J. J., & Tulsky, D. S. (2020). Development of Composite Scores for the TBI-QOL. Arch Phys Med Rehabil, 101(1), 43-53. https://doi.org/10.1016/j.apmr.2018.05.036
| Characteristic | N | Moderate, N = 191 | Severe, N = 271 | p-value2 |
|---|---|---|---|---|
| Physical Health Index | 46 | 91 (14) | 97 (14) | 0.19 |
| Emotional Health Index | 46 | 97 (12) | 101 (15) | 0.26 |
| Cognitive Health Index | 46 | 93 (13) | 95 (16) | 0.74 |
| Social Health Index | 46 | 94 (12) | 91 (13) | 0.41 |
| Global Health Index | 46 | 93 (13) | 95 (14) | 0.52 |
| 1 Mean (SD) | ||||
| 2 Welch Two Sample t-test | ||||
Table 2 in dissertation
This table compares only the Personal and Environmental Protective factors and self-regulation outlined in the dissertation. Note that the Cognitive Health Composite score was not used as it includes executive functioning, which in this paper is considered a self-regulatory process. Therefore, general cognitive functioning was used which assesses memory and concentration.
| Characteristic | N | Moderate, N = 191 | Severe, N = 271 | p-value2 |
|---|---|---|---|---|
| Physical Health Index | 46 | 91 (14) | 97 (14) | 0.19 |
| Emotional Health Index | 46 | 97 (12) | 101 (15) | 0.26 |
| General Cognition | 46 | 36 (8) | 37 (10) | 0.74 |
| Extraversion | 46 | 7.16 (2.50) | 6.78 (2.29) | 0.60 |
| Agreeable | 46 | 7.11 (1.94) | 7.15 (2.11) | 0.94 |
| Consciousness | 46 | 8.16 (1.54) | 7.67 (1.92) | 0.34 |
| Neuroticism | 46 | 6.47 (2.20) | 6.15 (2.66) | 0.65 |
| Openness | 46 | 8.53 (2.09) | 7.37 (1.86) | 0.062 |
| Annual household income | 46 |
|
|
0.84 |
| <52K |
|
6 (32%) | 10 (37%) |
|
| 52K-156K |
|
9 (47%) | 13 (48%) |
|
| >156K |
|
4 (21%) | 4 (15%) |
|
| Marital status | 46 |
|
|
0.11 |
| Single |
|
5 (26%) | 15 (56%) |
|
| Married |
|
11 (58%) | 8 (30%) |
|
| Divorced |
|
3 (16%) | 4 (15%) |
|
| Widowed |
|
0 (0%) | 0 (0%) |
|
| Social Support | 46 | 84 (10) | 76 (11) | 0.019 |
| Executive function | 46 | 41 (10) | 42 (11) | 0.82 |
| Disinhibition | 46 | 32.3 (6.1) | 33.0 (5.9) | 0.67 |
| Apathy | 46 | 33 (8) | 34 (9) | 0.61 |
| Total score | 46 | 106 (19) | 109 (21) | 0.65 |
| 1 Mean (SD); n (%) | ||||
| 2 Welch Two Sample t-test; Pearson’s Chi-squared test | ||||
Below is the t-test for SPS total, which was significantly different between severity of injury
##
## Welch Two Sample t-test
##
## data: df_mod$spstotal and df_severe$spstotal
## t = 2.4526, df = 41.168, p-value = 0.01851
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 1.368493 14.124685
## sample estimates:
## mean of x mean of y
## 83.89474 76.14815
Descriptive statistics for each variable of interest for the data set including mean, median, SD, and IQR, kurtosis and se
| vars | n | mean | sd | median | trimmed | mad | min | max | range | skew | kurtosis | se | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| record_id* | 1 | 46 | 23.50 | 13.42 | 23.50 | 23.50 | 17.05 | 1.0 | 46.0 | 45.0 | 0.00 | -1.28 | 1.98 |
| age_current | 2 | 46 | 47.07 | 14.44 | 46.50 | 46.97 | 18.53 | 21.0 | 72.0 | 51.0 | 0.17 | -1.18 | 2.13 |
| age_injury | 3 | 46 | 38.04 | 14.95 | 34.50 | 37.32 | 17.05 | 18.0 | 66.0 | 48.0 | 0.36 | -1.27 | 2.20 |
| time_injury | 4 | 46 | 9.09 | 7.10 | 7.00 | 8.20 | 5.93 | 1.0 | 30.0 | 29.0 | 1.14 | 0.73 | 1.05 |
| gender | 5 | 46 | 1.52 | 0.55 | 1.50 | 1.50 | 0.74 | 1.0 | 3.0 | 2.0 | 0.31 | -1.15 | 0.08 |
| race* | 6 | 46 | 4.57 | 1.05 | 5.00 | 4.84 | 0.00 | 1.0 | 5.0 | 4.0 | -2.39 | 4.56 | 0.15 |
| edu | 7 | 46 | 15.26 | 2.37 | 16.00 | 15.29 | 2.97 | 10.0 | 20.0 | 10.0 | -0.19 | -0.68 | 0.35 |
| work_current | 8 | 46 | 0.41 | 0.50 | 0.00 | 0.39 | 0.00 | 0.0 | 1.0 | 1.0 | 0.34 | -1.92 | 0.07 |
| hours_work | 9 | 19 | 30.50 | 13.04 | 40.00 | 31.50 | 0.00 | 4.0 | 40.0 | 36.0 | -0.74 | -1.19 | 2.99 |
| occ_years_pos | 10 | 18 | 7.43 | 9.21 | 3.50 | 6.47 | 5.04 | 0.1 | 30.0 | 29.9 | 1.47 | 1.14 | 2.17 |
| diff_occ | 11 | 19 | 0.53 | 0.51 | 1.00 | 0.53 | 0.00 | 0.0 | 1.0 | 1.0 | -0.10 | -2.09 | 0.12 |
| no_occ_stat | 12 | 27 | 3.19 | 0.74 | 3.00 | 3.13 | 0.00 | 2.0 | 5.0 | 3.0 | 1.40 | 1.67 | 0.14 |
| income | 13 | 46 | 1.83 | 0.71 | 2.00 | 1.79 | 1.48 | 1.0 | 3.0 | 2.0 | 0.25 | -1.05 | 0.10 |
| house_size | 14 | 46 | 2.11 | 1.23 | 2.00 | 1.92 | 1.48 | 1.0 | 7.0 | 6.0 | 1.68 | 3.66 | 0.18 |
| children | 15 | 46 | 0.43 | 0.50 | 0.00 | 0.42 | 0.00 | 0.0 | 1.0 | 1.0 | 0.25 | -1.98 | 0.07 |
| num_child | 16 | 20 | 2.00 | 1.26 | 2.00 | 1.75 | 1.48 | 1.0 | 5.0 | 4.0 | 1.21 | 0.49 | 0.28 |
| severity | 17 | 46 | 2.59 | 0.50 | 3.00 | 2.61 | 0.00 | 2.0 | 3.0 | 1.0 | -0.34 | -1.92 | 0.07 |
| mech_injury | 18 | 46 | 2.39 | 1.39 | 2.00 | 2.26 | 1.48 | 1.0 | 5.0 | 4.0 | 0.71 | -0.85 | 0.20 |
| mech_injury_other* | 19 | 6 | 1.17 | 0.41 | 1.00 | 1.17 | 0.00 | 1.0 | 2.0 | 1.0 | 1.36 | -0.08 | 0.17 |
| substance | 20 | 46 | 2.70 | 2.99 | 2.00 | 2.24 | 2.97 | 0.0 | 13.0 | 13.0 | 1.33 | 1.75 | 0.44 |
| acsg_prev | 21 | 46 | 69.33 | 10.20 | 68.00 | 69.11 | 10.38 | 48.0 | 93.0 | 45.0 | 0.28 | -0.61 | 1.50 |
| acsg_curr | 22 | 46 | 52.59 | 14.19 | 49.25 | 51.74 | 13.94 | 29.5 | 85.5 | 56.0 | 0.54 | -0.42 | 2.09 |
| acsg_retain | 23 | 46 | 75.98 | 16.47 | 74.50 | 76.18 | 14.08 | 43.0 | 107.0 | 64.0 | -0.09 | -0.45 | 2.43 |
| acsi_prev | 24 | 46 | 21.46 | 2.94 | 21.00 | 21.55 | 2.97 | 12.0 | 26.0 | 14.0 | -0.50 | 0.55 | 0.43 |
| acsi_curr | 25 | 46 | 17.26 | 4.61 | 17.00 | 16.97 | 5.93 | 10.5 | 26.0 | 15.5 | 0.41 | -1.07 | 0.68 |
| acsi_retain | 26 | 46 | 80.67 | 18.24 | 82.50 | 81.26 | 22.98 | 42.0 | 110.0 | 68.0 | -0.20 | -1.08 | 2.69 |
| acsl_prev | 27 | 46 | 21.54 | 5.10 | 21.00 | 21.58 | 5.93 | 11.0 | 32.0 | 21.0 | -0.01 | -0.85 | 0.75 |
| acsl_curr | 28 | 46 | 17.18 | 5.39 | 16.00 | 16.92 | 5.56 | 8.5 | 28.0 | 19.5 | 0.43 | -1.00 | 0.79 |
| acsl_retain | 29 | 46 | 80.48 | 18.46 | 79.00 | 80.68 | 17.79 | 44.0 | 122.0 | 78.0 | -0.02 | -0.52 | 2.72 |
| acsf_prev | 30 | 46 | 12.98 | 4.34 | 13.00 | 13.08 | 4.45 | 4.0 | 20.0 | 16.0 | -0.22 | -0.95 | 0.64 |
| acsf_curr | 31 | 46 | 8.33 | 3.93 | 8.25 | 8.14 | 3.34 | 1.0 | 18.0 | 17.0 | 0.32 | -0.11 | 0.58 |
| acsf_retain | 32 | 46 | 67.00 | 33.19 | 63.00 | 63.68 | 22.98 | 9.0 | 200.0 | 191.0 | 1.54 | 3.98 | 4.89 |
| acss_prev | 33 | 46 | 13.33 | 1.51 | 13.50 | 13.42 | 1.48 | 9.0 | 17.0 | 8.0 | -0.44 | 0.44 | 0.22 |
| acss_curr | 34 | 46 | 9.82 | 2.71 | 10.00 | 9.88 | 2.59 | 4.5 | 15.0 | 10.5 | -0.26 | -0.62 | 0.40 |
| acss_retain | 35 | 46 | 73.67 | 18.81 | 73.00 | 74.24 | 19.27 | 35.0 | 109.0 | 74.0 | -0.20 | -0.85 | 2.77 |
| activity_card_sort_complete | 36 | 46 | 2.00 | 0.00 | 2.00 | 2.00 | 0.00 | 2.0 | 2.0 | 0.0 | NaN | NaN | 0.00 |
| spstotal | 37 | 46 | 79.35 | 11.29 | 82.00 | 79.87 | 14.83 | 55.0 | 96.0 | 41.0 | -0.33 | -1.19 | 1.67 |
| bfi_extraversion | 38 | 46 | 6.93 | 2.36 | 7.00 | 7.05 | 2.97 | 2.0 | 10.0 | 8.0 | -0.23 | -1.02 | 0.35 |
| bfi_agreeable | 39 | 46 | 7.13 | 2.02 | 7.00 | 7.21 | 2.97 | 3.0 | 10.0 | 7.0 | -0.40 | -0.92 | 0.30 |
| bfi_consciousness | 40 | 46 | 7.87 | 1.77 | 8.00 | 8.03 | 1.48 | 3.0 | 10.0 | 7.0 | -0.70 | -0.33 | 0.26 |
| bfi_neuroticism | 41 | 46 | 6.28 | 2.46 | 6.00 | 6.32 | 2.97 | 2.0 | 10.0 | 8.0 | -0.02 | -1.15 | 0.36 |
| bfi_openness | 42 | 46 | 7.85 | 2.02 | 8.00 | 8.03 | 2.97 | 2.0 | 10.0 | 8.0 | -0.54 | -0.41 | 0.30 |
| frsbe_exec | 43 | 46 | 41.83 | 10.09 | 43.00 | 41.53 | 10.38 | 24.0 | 63.0 | 39.0 | 0.11 | -0.82 | 1.49 |
| frsbe_apathy | 44 | 46 | 33.28 | 8.61 | 32.00 | 32.82 | 10.38 | 18.0 | 53.0 | 35.0 | 0.43 | -0.71 | 1.27 |
| frsbe_disinhib | 45 | 46 | 32.72 | 5.95 | 32.00 | 32.58 | 6.67 | 21.0 | 46.0 | 25.0 | 0.17 | -0.77 | 0.88 |
| frsbe_total | 46 | 46 | 107.83 | 20.18 | 109.00 | 107.34 | 22.98 | 72.0 | 150.0 | 78.0 | 0.21 | -0.89 | 2.97 |
| frsbe_complete | 47 | 46 | 2.00 | 0.00 | 2.00 | 2.00 | 0.00 | 2.0 | 2.0 | 0.0 | NaN | NaN | 0.00 |
| tbiqol_part_sra_tscore | 48 | 46 | 46.33 | 6.70 | 46.00 | 45.81 | 5.78 | 32.1 | 64.1 | 32.0 | 0.83 | 1.10 | 0.99 |
| tbiqol_anger_tscore | 49 | 46 | 51.13 | 9.95 | 51.60 | 51.04 | 11.49 | 33.1 | 69.9 | 36.8 | 0.07 | -1.04 | 1.47 |
| tbiqol_anxiety_tscore | 50 | 46 | 55.64 | 9.23 | 56.05 | 55.89 | 9.56 | 36.1 | 73.0 | 36.9 | -0.23 | -0.73 | 1.36 |
| tbiqol_comm_tscore | 51 | 46 | 46.25 | 9.56 | 45.55 | 46.14 | 8.97 | 29.2 | 65.5 | 36.3 | 0.15 | -0.87 | 1.41 |
| tbiqol_depression_tscore | 52 | 46 | 53.95 | 9.78 | 53.85 | 54.10 | 10.16 | 33.6 | 74.0 | 40.4 | -0.08 | -0.72 | 1.44 |
| tbiqol_dyscontrol_tscore | 53 | 46 | 50.79 | 8.19 | 52.30 | 51.19 | 7.56 | 33.2 | 66.8 | 33.6 | -0.42 | -0.44 | 1.21 |
| tbiqol_execfunc_tscore | 54 | 46 | 35.48 | 6.06 | 34.30 | 35.22 | 5.34 | 24.3 | 50.8 | 26.5 | 0.38 | -0.51 | 0.89 |
| tbiqol_fatigue_tscore | 55 | 46 | 54.99 | 8.59 | 54.65 | 55.06 | 8.23 | 37.9 | 72.5 | 34.6 | 0.01 | -0.70 | 1.27 |
| tbiqol_genconcern_tscore | 56 | 46 | 36.16 | 8.80 | 35.85 | 35.99 | 8.90 | 19.7 | 53.8 | 34.1 | 0.18 | -0.72 | 1.30 |
| tbiqol_grief_tscore | 57 | 46 | 52.53 | 9.40 | 53.65 | 53.07 | 7.26 | 30.7 | 70.3 | 39.6 | -0.60 | -0.12 | 1.39 |
| tbiqol_headache_tscore | 58 | 46 | 49.44 | 9.22 | 49.50 | 48.98 | 13.20 | 38.5 | 67.1 | 28.6 | 0.14 | -1.33 | 1.36 |
| tbiqol_mobility_tscore | 59 | 46 | 45.69 | 8.75 | 44.40 | 45.26 | 8.45 | 31.5 | 63.6 | 32.1 | 0.46 | -0.72 | 1.29 |
| tbiqol_pain_tscore | 60 | 46 | 55.26 | 10.73 | 57.05 | 55.24 | 11.49 | 38.4 | 74.8 | 36.4 | -0.24 | -1.09 | 1.58 |
| tbiqol_posaffect_tscore | 61 | 46 | 50.09 | 7.43 | 49.65 | 49.95 | 7.71 | 35.4 | 68.9 | 33.5 | 0.21 | -0.51 | 1.10 |
| tbiqol_resilience_tscore | 62 | 46 | 48.45 | 8.09 | 49.10 | 48.12 | 7.78 | 33.4 | 73.6 | 40.2 | 0.44 | 0.43 | 1.19 |
| tbiqol_selfesteem_tscore | 63 | 46 | 48.06 | 10.73 | 48.45 | 48.10 | 10.75 | 28.4 | 66.0 | 37.6 | -0.01 | -0.97 | 1.58 |
| tbiqol_satissra_tscore | 64 | 46 | 45.23 | 6.25 | 45.10 | 44.79 | 4.60 | 34.7 | 63.2 | 28.5 | 0.88 | 1.18 | 0.92 |
| tbiqol_stigma_tscore | 65 | 45 | 50.98 | 7.39 | 52.00 | 51.61 | 6.08 | 33.5 | 62.3 | 28.8 | -0.73 | -0.12 | 1.10 |
| tbiqol_ue_tscore | 66 | 46 | 44.15 | 8.56 | 42.50 | 44.05 | 8.30 | 27.9 | 58.1 | 30.2 | 0.39 | -0.91 | 1.26 |
| marital_status | 67 | 46 | 1.72 | 0.72 | 2.00 | 1.66 | 1.48 | 1.0 | 3.0 | 2.0 | 0.46 | -1.03 | 0.11 |
| phys_health | 68 | 46 | 110.25 | 17.18 | 110.70 | 110.24 | 20.90 | 82.6 | 143.3 | 60.7 | -0.06 | -1.11 | 2.53 |
| phys_health_index | 69 | 46 | 94.57 | 13.85 | 95.00 | 94.74 | 16.31 | 64.0 | 117.0 | 53.0 | -0.12 | -0.98 | 2.04 |
| emo_health | 70 | 46 | 160.72 | 25.68 | 158.45 | 161.31 | 31.65 | 115.1 | 201.3 | 86.2 | -0.10 | -1.28 | 3.79 |
| emo_health_index | 71 | 46 | 99.33 | 14.10 | 101.00 | 99.11 | 17.79 | 77.0 | 123.0 | 46.0 | 0.05 | -1.35 | 2.08 |
| cog_health | 72 | 46 | 71.64 | 14.36 | 70.35 | 71.32 | 14.53 | 44.0 | 104.6 | 60.6 | 0.28 | -0.61 | 2.12 |
| cog_health_index | 73 | 46 | 94.11 | 14.73 | 93.00 | 94.13 | 16.31 | 62.0 | 123.0 | 61.0 | 0.02 | -0.75 | 2.17 |
| soc_health | 74 | 46 | 91.56 | 12.05 | 90.15 | 90.89 | 10.38 | 69.6 | 127.3 | 57.7 | 0.67 | 0.59 | 1.78 |
| soc_health_index | 75 | 46 | 92.65 | 12.23 | 92.50 | 92.89 | 10.38 | 64.0 | 122.0 | 58.0 | -0.16 | 0.27 | 1.80 |
| glob_health | 76 | 46 | 380.65 | 45.57 | 380.00 | 379.84 | 55.60 | 303.0 | 468.0 | 165.0 | 0.08 | -1.13 | 6.72 |
| glob_health_index | 77 | 46 | 94.37 | 13.44 | 95.00 | 94.11 | 15.57 | 71.0 | 120.0 | 49.0 | 0.04 | -1.06 | 1.98 |
Outcome variable: ACS3 for all scores mean(sd)
| Characteristic | N = 461 |
|---|---|
| ACS Global Before | 69 (10) |
| ACS Global Current | 53 (14) |
| Global Retained (%) | 76 (16) |
| ACS IADL Before | 21.46 (2.94) |
| ACS IADL Current | 17.3 (4.6) |
| IADL Retained (%) | 81 (18) |
| ACS Leisure Before | 21.5 (5.1) |
| ACS Leisure Current | 17.2 (5.4) |
| Leisure Retained (%) | 80 (18) |
| ACS Fitness Before | 13.0 (4.3) |
| ACS Fitness Current | 8.3 (3.9) |
| Fitness Retained (%) | 67 (33) |
| ACS Social Before | 13.33 (1.51) |
| ACS Social Current | 9.82 (2.71) |
| Social Retained (%) | 74 (19) |
| 1 Mean (SD) | |
Correlation of all variables of interest with TBI QOL subscores. While too small to read in HTML print out, nice reference during analysis
Correlation matrix of PPF only (using composite TBIQOL Scores)
Matrix with heat map for all included variables in dissertation. Figure 4 in dissertation
Below is the breakdown of all TBIQOL sub scores with the ACS3. While not included in this study, helpful for discussion and future publications.
TBI QOL subscales with the FrSBe
Research Question 1 1. What is the relationship between protective factors and self-regulation with resiliency-related outcomes such as re-engagement in meaningful activities? a. To what extent do protective factors and self-regulation predict resiliency-related outcomes in the TBI population? Hypothesis: Higher self-regulation will be associated with better resiliency-related outcomes b. To what extent does self-regulation mediate or moderate the influence of protective factors on resiliency-related outcomes after TBI? Hypothesis: Self-regulation will impact the relationship between protective factors and resiliency-related outcomes
First, we’ll look at the hierarchical linear model as outlined in Chapter 3. Then, to dive deeper, a “post hoc” analysis of each subscale of the ACS and use AIC to determine model of best fit.
In this section, we’ll do the original hierarchical model with protective and environmental protective factors in the first step and then total self-regulation score added for the second. *note that the cognitive composite score is not included as it includes exec functioning, which in this paper is seen as a self-regulatory process. therefore, gen concerns (memory and concentration) is used as cognitive protective factor
step1 <- lm(acsg_retain~age_current, data=df)
summary(step1)
##
## Call:
## lm(formula = acsg_retain ~ age_current, data = df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -31.645 -11.542 0.093 7.501 36.764
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 92.7375 8.0352 11.54 6.7e-15 ***
## age_current -0.3561 0.1634 -2.18 0.0347 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 15.82 on 44 degrees of freedom
## Multiple R-squared: 0.09745, Adjusted R-squared: 0.07693
## F-statistic: 4.751 on 1 and 44 DF, p-value: 0.03468
step2<- lm(acsg_retain~age_current+phys_health_index+tbiqol_genconcern_tscore+emo_health_index+ spstotal, data=df)
summary(step2)
##
## Call:
## lm(formula = acsg_retain ~ age_current + phys_health_index +
## tbiqol_genconcern_tscore + emo_health_index + spstotal, data = df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -25.997 -10.216 -1.356 10.269 30.600
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 43.23068 25.86980 1.671 0.1025
## age_current -0.13302 0.16854 -0.789 0.4346
## phys_health_index 0.07379 0.23057 0.320 0.7506
## tbiqol_genconcern_tscore 0.80590 0.37270 2.162 0.0366 *
## emo_health_index -0.09498 0.22396 -0.424 0.6738
## spstotal 0.15527 0.24641 0.630 0.5322
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 14.7 on 40 degrees of freedom
## Multiple R-squared: 0.2916, Adjusted R-squared: 0.203
## F-statistic: 3.293 on 5 and 40 DF, p-value: 0.0139
#Nested Model Comparison
anova(step1, step2)
## Analysis of Variance Table
##
## Model 1: acsg_retain ~ age_current
## Model 2: acsg_retain ~ age_current + phys_health_index + tbiqol_genconcern_tscore +
## emo_health_index + spstotal
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 44 11012.0
## 2 40 8643.4 4 2368.7 2.7404 0.04176 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#change in R-squared
summary(step2)$r.squared - summary(step1)$r.squared
## [1] 0.1941371
step3<- lm(acsg_retain~age_current+phys_health_index+tbiqol_genconcern_tscore+emo_health_index+ spstotal+frsbe_total, data=df)
summary(step3)
##
## Call:
## lm(formula = acsg_retain ~ age_current + phys_health_index +
## tbiqol_genconcern_tscore + emo_health_index + spstotal +
## frsbe_total, data = df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -25.765 -9.412 -1.115 9.607 30.674
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 54.89809 39.18755 1.401 0.1691
## age_current -0.13795 0.17078 -0.808 0.4241
## phys_health_index 0.07706 0.23318 0.330 0.7428
## tbiqol_genconcern_tscore 0.75110 0.40085 1.874 0.0685 .
## emo_health_index -0.11110 0.22992 -0.483 0.6316
## spstotal 0.13131 0.25616 0.513 0.6111
## frsbe_total -0.05806 0.14525 -0.400 0.6916
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 14.86 on 39 degrees of freedom
## Multiple R-squared: 0.2945, Adjusted R-squared: 0.1859
## F-statistic: 2.713 on 6 and 39 DF, p-value: 0.02676
#Nested Model Comparison
anova(step2, step3)
## Analysis of Variance Table
##
## Model 1: acsg_retain ~ age_current + phys_health_index + tbiqol_genconcern_tscore +
## emo_health_index + spstotal
## Model 2: acsg_retain ~ age_current + phys_health_index + tbiqol_genconcern_tscore +
## emo_health_index + spstotal + frsbe_total
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 40 8643.4
## 2 39 8608.1 1 35.263 0.1598 0.6916
#change in R-squared
summary(step3)$r.squared - summary(step2)$r.squared
## [1] 0.002890204
Test for multicollinearity
## age_current phys_health_index tbiqol_genconcern_tscore
## 1.239106 2.126709 2.538554
## emo_health_index spstotal frsbe_total
## 2.142136 1.706537 1.751156
As we have a smaller n and need to be parsimonious with the variables we use in the regression model, we’ll look at several models based on correlations (higher correlations added to the models) and then calculate the AIC. The lower AIC, the better the fit and that model will be used
Note that all assumptions were tested for each model and were met
##
## Model selection based on AICc:
##
## K AICc Delta_AICc AICcWt Cum.Wt LL
## model6 7 397.09 0.00 0.35 0.35 -190.07
## model4 7 397.61 0.51 0.27 0.63 -190.33
## model3 8 399.11 2.01 0.13 0.76 -189.61
## model5 8 399.87 2.77 0.09 0.85 -189.99
## model2 8 400.09 3.00 0.08 0.93 -190.10
## model1 8 400.25 3.15 0.07 1.00 -190.18
##
## Model selection based on AICc:
##
## K AICc Delta_AICc AICcWt Cum.Wt LL
## model7 6 397.56 0.00 0.28 0.28 -191.70
## model6 6 397.58 0.02 0.28 0.57 -191.71
## model3 6 397.88 0.32 0.24 0.81 -191.86
## model1 7 399.92 2.35 0.09 0.90 -191.48
## model4 7 400.36 2.80 0.07 0.97 -191.71
## model2 8 402.73 5.17 0.02 0.99 -191.42
## model5 7 404.08 6.52 0.01 1.00 -193.57
##
## Call:
## lm(formula = acsl_retain ~ tbiqol_mobility_tscore + tbiqol_genconcern_tscore +
## tbiqol_comm_tscore + frsbe_apathy, data = df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -34.458 -10.970 -0.449 10.394 35.565
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 44.1102 23.5848 1.870 0.0686 .
## tbiqol_mobility_tscore 0.6630 0.3502 1.893 0.0654 .
## tbiqol_genconcern_tscore 0.8003 0.3665 2.184 0.0347 *
## tbiqol_comm_tscore -0.3487 0.3847 -0.906 0.3700
## frsbe_apathy -0.2025 0.3330 -0.608 0.5466
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 16.55 on 41 degrees of freedom
## Multiple R-squared: 0.2675, Adjusted R-squared: 0.196
## F-statistic: 3.743 on 4 and 41 DF, p-value: 0.01098
##
## Model selection based on AICc:
##
## K AICc Delta_AICc AICcWt Cum.Wt LL
## model2 6 450.93 0.00 0.81 0.81 -218.36
## model1 8 455.12 4.19 0.10 0.91 -217.56
## model5 8 455.54 4.62 0.08 0.99 -217.77
## model4 6 459.70 8.77 0.01 1.00 -222.77
## model3 7 461.23 10.30 0.00 1.00 -222.14
##
## Call:
## lm(formula = acsf_retain ~ phys_health_index + tbiqol_genconcern_tscore +
## tbiqol_anxiety_tscore + tbiqol_stigma_tscore, data = df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -61.962 -17.262 -7.947 11.301 124.325
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 70.6891 70.1111 1.008 0.319
## phys_health_index 0.3343 0.4807 0.695 0.491
## tbiqol_genconcern_tscore 0.3498 0.7653 0.457 0.650
## tbiqol_anxiety_tscore -0.1997 0.7241 -0.276 0.784
## tbiqol_stigma_tscore -0.7217 0.8918 -0.809 0.423
##
## Residual standard error: 32.86 on 40 degrees of freedom
## (1 observation deleted due to missingness)
## Multiple R-squared: 0.1283, Adjusted R-squared: 0.04115
## F-statistic: 1.472 on 4 and 40 DF, p-value: 0.2288
For moderation, looking at personal protective factors and environmental protective factors from original model
##
## Call:
## lm(formula = acsg_retain ~ phys_health_index * frsbe_total, data = df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -31.431 -8.709 -1.636 10.093 33.238
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 18.928754 103.340073 0.183 0.856
## phys_health_index 0.815796 1.032293 0.790 0.434
## frsbe_total 0.241520 0.923153 0.262 0.795
## phys_health_index:frsbe_total -0.004570 0.009366 -0.488 0.628
##
## Residual standard error: 15.34 on 42 degrees of freedom
## Multiple R-squared: 0.1898, Adjusted R-squared: 0.1319
## F-statistic: 3.28 on 3 and 42 DF, p-value: 0.03006
##
## Call:
## lm(formula = acsg_retain ~ tbiqol_genconcern_tscore * frsbe_total,
## data = df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -28.3975 -9.9418 0.9413 8.4603 31.4643
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 43.336712 52.829896 0.820 0.417
## tbiqol_genconcern_tscore 1.080987 1.386257 0.780 0.440
## frsbe_total 0.003913 0.476477 0.008 0.993
## tbiqol_genconcern_tscore:frsbe_total -0.001809 0.013296 -0.136 0.892
##
## Residual standard error: 14.51 on 42 degrees of freedom
## Multiple R-squared: 0.2752, Adjusted R-squared: 0.2235
## F-statistic: 5.317 on 3 and 42 DF, p-value: 0.003381
##
## Call:
## lm(formula = acsg_retain ~ emo_health_index * frsbe_total, data = df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -31.984 -7.556 0.125 9.355 30.142
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -76.646620 110.391140 -0.694 0.491
## emo_health_index 1.681344 1.027245 1.637 0.109
## frsbe_total 1.182751 0.955996 1.237 0.223
## emo_health_index:frsbe_total -0.013429 0.009071 -1.480 0.146
##
## Residual standard error: 15.39 on 42 degrees of freedom
## Multiple R-squared: 0.1842, Adjusted R-squared: 0.1259
## F-statistic: 3.16 on 3 and 42 DF, p-value: 0.03435
##
## Call:
## lm(formula = acsg_retain ~ spstotal * frsbe_total, data = df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -32.255 -8.786 0.385 7.966 29.966
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 36.98213 115.10290 0.321 0.750
## spstotal 0.78362 1.42193 0.551 0.584
## frsbe_total 0.17807 1.04175 0.171 0.865
## spstotal:frsbe_total -0.00502 0.01318 -0.381 0.705
##
## Residual standard error: 15.74 on 42 degrees of freedom
## Multiple R-squared: 0.1476, Adjusted R-squared: 0.08675
## F-statistic: 2.425 on 3 and 42 DF, p-value: 0.07901
There was no moderating effect of apathy on any of the predictors.
Here we look at mediation effect of the total FrSBe scores on the personal and environmental factors used in the post hoc model (mobility, general cog functioning, anxiety, depression, and social support)
How to read: ACME = indirect effect ADE = direct effect ACME + ADE = total effect
# Initial Model
model1 <- lm(acsg_retain ~ phys_health_index, df) # Y ~ X, DV predicted by IV - no mediation considered - total effect
summary(model1)
##
## Call:
## lm(formula = acsg_retain ~ phys_health_index, data = df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -32.165 -8.827 -2.595 11.831 33.431
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 35.3344 15.9664 2.213 0.0321 *
## phys_health_index 0.4298 0.1671 2.572 0.0136 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 15.53 on 44 degrees of freedom
## Multiple R-squared: 0.1307, Adjusted R-squared: 0.111
## F-statistic: 6.616 on 1 and 44 DF, p-value: 0.01356
# Mediation paths
medmodel1 <- lm(frsbe_total ~ phys_health_index, df) # M ~ X, mediator predicted by X
outputmodel1 <- lm(acsg_retain ~ phys_health_index + frsbe_total, df) # Y ~ X + M, DV predicted by mediator, adjusting for IV
summary(medmodel1)
##
## Call:
## lm(formula = frsbe_total ~ phys_health_index, data = df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -32.904 -14.259 -1.361 12.529 48.322
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 158.6667 19.5030 8.135 2.58e-10 ***
## phys_health_index -0.5376 0.2041 -2.634 0.0116 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 18.96 on 44 degrees of freedom
## Multiple R-squared: 0.1362, Adjusted R-squared: 0.1166
## F-statistic: 6.938 on 1 and 44 DF, p-value: 0.0116
summary(outputmodel1)
##
## Call:
## lm(formula = acsg_retain ~ phys_health_index + frsbe_total, data = df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -31.877 -8.530 -0.878 9.844 34.180
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 67.8622 24.7442 2.743 0.00885 **
## phys_health_index 0.3196 0.1761 1.815 0.07649 .
## frsbe_total -0.2050 0.1209 -1.696 0.09709 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 15.2 on 43 degrees of freedom
## Multiple R-squared: 0.1852, Adjusted R-squared: 0.1473
## F-statistic: 4.888 on 2 and 43 DF, p-value: 0.01223
# Mediation test
mediation <- mediate(medmodel1, # Mediator model
outputmodel1, # Outcome model
boot = T, # Ask for bootstrapped confidence intervals
treat="phys_health_index", # Name of the x variable
mediator="frsbe_total" # Name of the m variable
)
# if you don't want bootstrap, just delete 'sims' line and set boot = F
summary(mediation)
##
## Causal Mediation Analysis
##
## Nonparametric Bootstrap Confidence Intervals with the Percentile Method
##
## Estimate 95% CI Lower 95% CI Upper p-value
## ACME 0.110217 0.008039 0.27 0.036 *
## ADE 0.319580 -0.020897 0.66 0.074 .
## Total Effect 0.429797 0.108945 0.79 0.018 *
## Prop. Mediated 0.256439 -0.000922 1.05 0.054 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Sample Size Used: 46
##
##
## Simulations: 1000
plot(mediation)
There is a significant indirect effect and an insignificant direct
effect, indicating total mediation
## [1] "-0.54"
## [1] "-0.21"
#### Cognitive Health
##
## Call:
## lm(formula = acsg_retain ~ tbiqol_genconcern_tscore, data = df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -28.3103 -10.7011 0.1933 9.3422 31.1265
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 40.7291 8.9525 4.549 4.2e-05 ***
## tbiqol_genconcern_tscore 0.9747 0.2407 4.050 0.000205 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 14.21 on 44 degrees of freedom
## Multiple R-squared: 0.2715, Adjusted R-squared: 0.255
## F-statistic: 16.4 on 1 and 44 DF, p-value: 0.0002048
##
## Call:
## lm(formula = frsbe_total ~ tbiqol_genconcern_tscore, data = df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -33.175 -9.996 0.624 10.540 40.890
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 156.0357 10.4557 14.923 < 2e-16 ***
## tbiqol_genconcern_tscore -1.3331 0.2811 -4.743 2.25e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 16.6 on 44 degrees of freedom
## Multiple R-squared: 0.3383, Adjusted R-squared: 0.3232
## F-statistic: 22.49 on 1 and 44 DF, p-value: 2.245e-05
##
## Call:
## lm(formula = acsg_retain ~ tbiqol_genconcern_tscore + frsbe_total,
## data = df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -28.5347 -10.1387 0.7124 8.4955 31.4666
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 49.8410 22.2441 2.241 0.03027 *
## tbiqol_genconcern_tscore 0.8969 0.2986 3.004 0.00443 **
## frsbe_total -0.0584 0.1303 -0.448 0.65621
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 14.34 on 43 degrees of freedom
## Multiple R-squared: 0.2749, Adjusted R-squared: 0.2412
## F-statistic: 8.152 on 2 and 43 DF, p-value: 0.0009958
##
## Causal Mediation Analysis
##
## Nonparametric Bootstrap Confidence Intervals with the Percentile Method
##
## Estimate 95% CI Lower 95% CI Upper p-value
## ACME 0.0778 -0.1601 0.32 0.46
## ADE 0.8969 0.4445 1.38 <2e-16 ***
## Total Effect 0.9747 0.5645 1.46 <2e-16 ***
## Prop. Mediated 0.0799 -0.2033 0.35 0.46
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Sample Size Used: 46
##
##
## Simulations: 1000
There is no significant indirect effect. No mediation
##
## Call:
## lm(formula = acsg_retain ~ emo_health_index, data = df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -35.342 -9.104 -0.366 9.243 31.047
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 41.4375 16.8596 2.458 0.0180 *
## emo_health_index 0.3478 0.1681 2.069 0.0445 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 15.9 on 44 degrees of freedom
## Multiple R-squared: 0.08865, Adjusted R-squared: 0.06794
## F-statistic: 4.28 on 1 and 44 DF, p-value: 0.04447
##
## Call:
## lm(formula = frsbe_total ~ emo_health_index, data = df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -27.595 -12.448 -1.991 8.070 48.441
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 180.9026 18.5626 9.746 1.47e-12 ***
## emo_health_index -0.7357 0.1851 -3.975 0.000258 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 17.5 on 44 degrees of freedom
## Multiple R-squared: 0.2643, Adjusted R-squared: 0.2475
## F-statistic: 15.8 on 1 and 44 DF, p-value: 0.0002579
##
## Call:
## lm(formula = acsg_retain ~ emo_health_index + frsbe_total, data = df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -33.527 -8.166 0.086 7.921 31.359
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 81.0401 29.4160 2.755 0.00857 **
## emo_health_index 0.1867 0.1924 0.970 0.33728
## frsbe_total -0.2189 0.1344 -1.629 0.11071
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 15.61 on 43 degrees of freedom
## Multiple R-squared: 0.1416, Adjusted R-squared: 0.1017
## F-statistic: 3.546 on 2 and 43 DF, p-value: 0.03753
##
## Causal Mediation Analysis
##
## Nonparametric Bootstrap Confidence Intervals with the Percentile Method
##
## Estimate 95% CI Lower 95% CI Upper p-value
## ACME 0.1611 -0.0597 0.32 0.12
## ADE 0.1867 -0.2037 0.64 0.32
## Total Effect 0.3478 0.0113 0.68 0.04 *
## Prop. Mediated 0.4632 -0.4442 3.36 0.16
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Sample Size Used: 46
##
##
## Simulations: 1000
There is no significant indirect effect indicating no mediation
## [1] "-0.74"
## [1] "-0.22"
#### SPS
##
## Call:
## lm(formula = acsg_retain ~ spstotal, data = df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -34.044 -8.542 0.400 8.817 31.509
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 40.7396 16.7745 2.429 0.0193 *
## spstotal 0.4441 0.2093 2.121 0.0396 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 15.86 on 44 degrees of freedom
## Multiple R-squared: 0.09279, Adjusted R-squared: 0.07218
## F-statistic: 4.501 on 1 and 44 DF, p-value: 0.03955
##
## Call:
## lm(formula = frsbe_total ~ spstotal, data = df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -38.16 -12.89 -1.67 10.30 36.27
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 179.3888 18.6288 9.630 2.11e-12 ***
## spstotal -0.9019 0.2325 -3.879 0.000346 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 17.61 on 44 degrees of freedom
## Multiple R-squared: 0.2549, Adjusted R-squared: 0.2379
## F-statistic: 15.05 on 1 and 44 DF, p-value: 0.0003463
##
## Call:
## lm(formula = acsg_retain ~ spstotal + frsbe_total, data = df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -32.888 -8.280 0.220 7.625 29.910
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 79.3728 29.0439 2.733 0.00908 **
## spstotal 0.2499 0.2382 1.049 0.30003
## frsbe_total -0.2154 0.1333 -1.615 0.11358
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 15.58 on 43 degrees of freedom
## Multiple R-squared: 0.1447, Adjusted R-squared: 0.1049
## F-statistic: 3.637 on 2 and 43 DF, p-value: 0.03473
##
## Causal Mediation Analysis
##
## Nonparametric Bootstrap Confidence Intervals with the Percentile Method
##
## Estimate 95% CI Lower 95% CI Upper p-value
## ACME 0.1942 0.0148 0.45 0.026 *
## ADE 0.2499 -0.2502 0.72 0.334
## Total Effect 0.4441 -0.0161 0.86 0.064 .
## Prop. Mediated 0.4374 -1.2758 2.70 0.090 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Sample Size Used: 46
##
##
## Simulations: 1000
There is a significant indirect effect and insignificant direct effect
indicating full mediation
## [1] "-0.9"
## [1] "-0.22"
To answer these questions, first look at descriptive statistics, then regression model with time since injury included, lastly, investigate what, if any, role time since injury has on protective factors and self-regulation
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## time_injury 1 3.5 7 9.09 11 30
First, looking just at the relationship between time since injury and re-engagement while controlling for age.
##
## Call:
## lm(formula = acsg_retain ~ age_current + time_injury, data = df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -27.815 -10.656 -1.068 6.706 40.845
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 89.6687 8.0476 11.142 2.93e-14 ***
## age_current -0.4019 0.1618 -2.484 0.0170 *
## time_injury 0.5751 0.3290 1.748 0.0876 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 15.46 on 43 degrees of freedom
## Multiple R-squared: 0.1573, Adjusted R-squared: 0.1181
## F-statistic: 4.014 on 2 and 43 DF, p-value: 0.02521
Controlling for age, there is no significant relationship between time since injury and re-engagement
##
## Call:
## lm(formula = acsi_retain ~ age_current + time_injury, data = df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -34.001 -14.491 -1.073 15.727 31.400
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 90.2276 9.2527 9.751 1.84e-12 ***
## age_current -0.3122 0.1861 -1.678 0.101
## time_injury 0.5659 0.3782 1.496 0.142
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 17.78 on 43 degrees of freedom
## Multiple R-squared: 0.09199, Adjusted R-squared: 0.04975
## F-statistic: 2.178 on 2 and 43 DF, p-value: 0.1256
##
## Call:
## lm(formula = acsl_retain ~ age_current + time_injury, data = df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -28.517 -12.231 0.136 12.241 53.013
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 96.6837 9.1006 10.624 1.33e-13 ***
## age_current -0.4482 0.1830 -2.449 0.0185 *
## time_injury 0.5379 0.3720 1.446 0.1555
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 17.49 on 43 degrees of freedom
## Multiple R-squared: 0.1422, Adjusted R-squared: 0.1023
## F-statistic: 3.564 on 2 and 43 DF, p-value: 0.03697
##
## Call:
## lm(formula = acsf_retain ~ age_current + time_injury, data = df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -48.889 -18.546 -5.539 9.106 129.208
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 80.4566 16.9992 4.733 2.41e-05 ***
## age_current -0.4788 0.3418 -1.401 0.168
## time_injury 0.9989 0.6949 1.438 0.158
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 32.66 on 43 degrees of freedom
## Multiple R-squared: 0.07462, Adjusted R-squared: 0.03158
## F-statistic: 1.734 on 2 and 43 DF, p-value: 0.1888
##
## Call:
## lm(formula = acsg_retain ~ age_current + phys_health_index +
## tbiqol_genconcern_tscore + emo_health_index + spstotal +
## frsbe_total + time_injury, data = df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -25.772 -7.984 -2.549 7.321 30.924
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 49.6084 36.9543 1.342 0.1874
## age_current -0.1810 0.1617 -1.119 0.2700
## phys_health_index 0.1234 0.2203 0.560 0.5786
## tbiqol_genconcern_tscore 0.6892 0.3782 1.822 0.0763 .
## emo_health_index -0.1499 0.2170 -0.691 0.4941
## spstotal 0.2162 0.2436 0.887 0.3804
## frsbe_total -0.1001 0.1378 -0.726 0.4722
## time_injury 0.7499 0.3060 2.451 0.0190 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 13.99 on 38 degrees of freedom
## Multiple R-squared: 0.3908, Adjusted R-squared: 0.2785
## F-statistic: 3.482 on 7 and 38 DF, p-value: 0.005578
#Nested Model Comparison
anova(step3, step4)
## Analysis of Variance Table
##
## Model 1: acsg_retain ~ age_current + phys_health_index + tbiqol_genconcern_tscore +
## emo_health_index + spstotal + frsbe_total
## Model 2: acsg_retain ~ age_current + phys_health_index + tbiqol_genconcern_tscore +
## emo_health_index + spstotal + frsbe_total + time_injury
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 39 8608.1
## 2 38 7433.2 1 1174.9 6.0062 0.01897 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#change in R-squared
summary(step4)$r.squared - summary(step3)$r.squared
## [1] 0.09629406
Because there was no significant relationship between time since injury and outcomes BUT it was a significant predictor in the model, I seperated time since recovery into three groups to examine relationships further. Early = 6mo-3years Mid = 3 to 10 years Later = >10 years
1= everything <=3 and 3 is everything >=10
| Characteristic | Early, N = 111 | Mid, N = 141 | Later, N = 211 |
|---|---|---|---|
| Age (years) | 41 (13) | 50 (17) | 48 (13) |
| Employment status |
|
|
|
| 0 | 4 (36%) | 11 (79%) | 12 (57%) |
| 1 | 7 (64%) | 3 (21%) | 9 (43%) |
| Substance use score | 1.82 (1.66) | 3.21 (3.53) | 2.81 (3.16) |
| Severity of Injury |
|
|
|
| 2 | 5 (45%) | 7 (50%) | 7 (33%) |
| 3 | 6 (55%) | 7 (50%) | 14 (67%) |
| Global ACS | 71 (14) | 73 (18) | 80 (16) |
| Social ACS | 70 (17) | 69 (14) | 79 (22) |
| IADL ACS | 74 (19) | 79 (20) | 85 (16) |
| Leisure ACS | 74 (17) | 79 (21) | 85 (17) |
| Fitness ACS | 61 (21) | 61 (31) | 74 (40) |
| 1 Mean (SD); n (%) | |||
##
## Early Mid Later
## 11 14 21
We see the counts of # participants in each group
Global ACS3 scores (ie, global re-engagement scores)
## # A tibble: 3 × 4
## time_injury_ex count mean sd
## <fct> <int> <dbl> <dbl>
## 1 Early 11 70.9 13.9
## 2 Mid 14 73.4 18.3
## 3 Later 21 80.4 16.0
## Df Sum Sq Mean Sq F value Pr(>F)
## time_injury_ex 2 786 393.0 1.48 0.239
## Residuals 43 11415 265.5
##
## Call:
## lm(formula = acsg_retain ~ age_current + time_injury_ex, data = df_time3)
##
## Residuals:
## Min 1Q Median 3Q Max
## -28.113 -11.124 -0.222 9.180 39.339
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 88.0693 8.1159 10.851 9.25e-14 ***
## age_current -0.4204 0.1635 -2.572 0.0137 *
## time_injury_exMid 6.3382 6.3566 0.997 0.3244
## time_injury_exLater 12.6313 5.8341 2.165 0.0361 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 15.32 on 42 degrees of freedom
## Multiple R-squared: 0.1917, Adjusted R-squared: 0.134
## F-statistic: 3.32 on 3 and 42 DF, p-value: 0.02874
Controlling for age, we see a significant relationship between time since injury and global re engagement- specifically between early and later recovery
IADL ACS3 scores (ie, IADL re-engagement scores)
## # A tibble: 3 × 4
## time_injury_ex count mean sd
## <fct> <int> <dbl> <dbl>
## 1 Early 11 73.5 19.1
## 2 Mid 14 79.1 20.2
## 3 Later 21 85.4 15.8
## Df Sum Sq Mean Sq F value Pr(>F)
## time_injury_ex 2 1067 533.3 1.649 0.204
## Residuals 43 13902 323.3
##
## Call:
## lm(formula = acsi_retain ~ age_current + time_injury_ex, data = df_time3)
##
## Residuals:
## Min 1Q Median 3Q Max
## -28.0032 -14.6952 -0.5134 14.7014 29.3986
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 87.7460 9.2599 9.476 5.46e-12 ***
## age_current -0.3479 0.1865 -1.865 0.0691 .
## time_injury_exMid 8.8166 7.2526 1.216 0.2309
## time_injury_exLater 14.4976 6.6564 2.178 0.0351 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 17.48 on 42 degrees of freedom
## Multiple R-squared: 0.1423, Adjusted R-squared: 0.08104
## F-statistic: 2.323 on 3 and 42 DF, p-value: 0.08878
We see a significant difference between early and late groups with IADl engagement when controlling for age
Leisure ACS3 scores (ie, leisure re-engagement scores)
## # A tibble: 3 × 4
## time_injury_ex count mean sd
## <fct> <int> <dbl> <dbl>
## 1 Early 11 74.3 17.0
## 2 Mid 14 78.8 21.0
## 3 Later 21 84.9 17.0
## Df Sum Sq Mean Sq F value Pr(>F)
## time_injury_ex 2 866 433.2 1.288 0.286
## Residuals 43 14461 336.3
##
## Call:
## lm(formula = acsl_retain ~ age_current + time_injury_ex, data = df_time3)
##
## Residuals:
## Min 1Q Median 3Q Max
## -30.295 -14.417 0.274 10.861 50.959
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 94.1180 9.0946 10.349 3.99e-13 ***
## age_current -0.4862 0.1832 -2.654 0.0112 *
## time_injury_exMid 9.0118 7.1232 1.265 0.2128
## time_injury_exLater 14.2382 6.5377 2.178 0.0351 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 17.17 on 42 degrees of freedom
## Multiple R-squared: 0.192, Adjusted R-squared: 0.1343
## F-statistic: 3.328 on 3 and 42 DF, p-value: 0.02851
We see a significant difference between early and late groups with Leisure engagement when controlling for age
Fitness ACS3 scores (ie, fitness re-engagement scores)
## # A tibble: 3 × 4
## time_injury_ex count mean sd
## <fct> <int> <dbl> <dbl>
## 1 Early 11 60.7 20.6
## 2 Mid 14 61.4 30.6
## 3 Later 21 74.0 39.5
## Df Sum Sq Mean Sq F value Pr(>F)
## time_injury_ex 2 1922 960.8 0.867 0.427
## Residuals 43 47652 1108.2
##
## Call:
## lm(formula = acsf_retain ~ age_current + time_injury_ex, data = df_time3)
##
## Residuals:
## Min 1Q Median 3Q Max
## -55.817 -19.104 -6.348 9.571 133.305
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 79.8850 17.4740 4.572 4.22e-05 ***
## age_current -0.4693 0.3520 -1.334 0.190
## time_injury_exMid 4.9728 13.6861 0.363 0.718
## time_injury_exLater 16.8475 12.5612 1.341 0.187
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 32.99 on 42 degrees of freedom
## Multiple R-squared: 0.07781, Adjusted R-squared: 0.01194
## F-statistic: 1.181 on 3 and 42 DF, p-value: 0.3284
This is exploratory and post hoc analysis- likely unable to report to avoid p-hacking, more for information gathering
###FrSBe
## Df Sum Sq Mean Sq F value Pr(>F)
## time_injury_ex 2 19 9.5 0.022 0.978
## Residuals 43 18302 425.6
## Df Sum Sq Mean Sq F value Pr(>F)
## time_injury_ex 2 219 109.3 0.559 0.576
## Residuals 43 8415 195.7
## Df Sum Sq Mean Sq F value Pr(>F)
## time_injury_ex 2 384 191.9 0.964 0.389
## Residuals 43 8560 199.1
## Df Sum Sq Mean Sq F value Pr(>F)
## time_injury_ex 2 25 12.48 0.155 0.857
## Residuals 43 3462 80.51
Below is the comparison of t-values for step 3 and step 4 to see if the inclusion of time since injury significantly changed the contribution of each variable in the model. (not sure this is an appropriate way to report in paper, but wanted to see…)
## Variable z_score p_value
## 1 age_current 1.32457593 0.1853118
## 2 phys_health_index -0.71621797 0.4738568
## 3 emo_health_index 0.65568756 0.5120252
## 4 tbiqol_genconcern_tscore 0.09340553 0.9255814
## 5 spstotal -1.06041170 0.2889573
## 6 frsbe_total 1.63066692 0.1029606
Create a sequence from 1 to 44 numbers <- 1:46
Randomly select 8 numbers selected_numbers <- sample(numbers, 8)
Format the selected numbers with leading zeros formatted_numbers <- sprintf(“%03d”, selected_numbers)
Print the selected numbers print(formatted_numbers)
“020” “007” “006” “019” “024” “041” “013” “037”
Social ACS3